
import os
from pickle import UnpicklingError
from typing import Dict, Tuple

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict

import transformers

from .utils import logging


lowerCamelCase_    :			Dict    =	logging.get_logger(__name__)







def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase=False    )	->						Tuple:
				try:
								import torch  # noqa: F401
				except ImportError:
								logger.error(
								    """Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see"""
								    """ https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"""
								    """ instructions."""    )
								raise

				if not is_sharded:
								UpperCamelCase_:				str						=    os.path.abspath(lowerCamelCase    )
								logger.info(F'''Loading PyTorch weights from {pt_path}'''    )

								UpperCamelCase_:				Union[str, Any]						=    torch.load(lowerCamelCase  ,     map_location="""cpu"""    )
								logger.info(F'''PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()    ):,} parameters.'''    )

								UpperCamelCase_:				Optional[Any]						=    convert_pytorch_state_dict_to_flax(lowerCamelCase  ,     lowerCamelCase    )
				else:
								# model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
								UpperCamelCase_:				Optional[int]						=    convert_pytorch_sharded_state_dict_to_flax(lowerCamelCase  ,     lowerCamelCase    )
				return flax_state_dict







def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     )	->						(Tuple[str], np.ndarray):

				def is_key_or_prefix_key_in_dict(lowerCamelCase    ) -> bool:
								return len(set(lowerCamelCase    ) & {key, (model_prefix,) + key}    ) > 0

				# layer norm
				UpperCamelCase_:				Optional[Any]						=    pt_tuple_key[:-1] + ("""scale""",)
				if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(lowerCamelCase    ):
								return renamed_pt_tuple_key, pt_tensor

				# batch norm layer mean
				UpperCamelCase_:				Tuple						=    pt_tuple_key[:-1] + ("""mean""",)
				if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(lowerCamelCase    ):
								return renamed_pt_tuple_key, pt_tensor

				# batch norm layer var
				UpperCamelCase_:				Optional[Any]						=    pt_tuple_key[:-1] + ("""var""",)
				if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(lowerCamelCase    ):
								return renamed_pt_tuple_key, pt_tensor

				# embedding
				UpperCamelCase_:				List[Any]						=    pt_tuple_key[:-1] + ("""embedding""",)
				if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(lowerCamelCase    ):
								return renamed_pt_tuple_key, pt_tensor

				# conv layer
				UpperCamelCase_:				Tuple						=    pt_tuple_key[:-1] + ("""kernel""",)
				if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(lowerCamelCase    ):
								UpperCamelCase_:				Tuple						=    pt_tensor.transpose(2  ,     3  ,     1  ,     0    )
								return renamed_pt_tuple_key, pt_tensor

				# linear layer
				UpperCamelCase_:				str						=    pt_tuple_key[:-1] + ("""kernel""",)
				if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(lowerCamelCase    ):
								UpperCamelCase_:				Dict						=    pt_tensor.T
								return renamed_pt_tuple_key, pt_tensor

				# old PyTorch layer norm weight
				UpperCamelCase_:				List[str]						=    pt_tuple_key[:-1] + ("""weight""",)
				if pt_tuple_key[-1] == "gamma":
								return renamed_pt_tuple_key, pt_tensor

				# old PyTorch layer norm bias
				UpperCamelCase_:				Union[str, Any]						=    pt_tuple_key[:-1] + ("""bias""",)
				if pt_tuple_key[-1] == "beta":
								return renamed_pt_tuple_key, pt_tensor

				# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
				UpperCamelCase_:				Optional[Any]						=    None
				if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
								UpperCamelCase_:				Union[str, Any]						=    pt_tuple_key[-2] + """_g"""
				elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
								UpperCamelCase_:				Optional[int]						=    pt_tuple_key[-2] + """_v"""
				if name is not None:
								UpperCamelCase_:				List[Any]						=    pt_tuple_key[:-3] + (name,)
								return renamed_pt_tuple_key, pt_tensor

				return pt_tuple_key, pt_tensor







def 							A__       (    lowerCamelCase  ,     lowerCamelCase    )	->						Union[str, Any]:
				# convert pytorch tensor to numpy
				UpperCamelCase_:				Optional[Any]						=    {k: v.numpy() for k, v in pt_state_dict.items()}

				UpperCamelCase_:				Dict						=    flax_model.base_model_prefix

				# use params dict if the model contains batch norm layers
				if "params" in flax_model.params:
								UpperCamelCase_:				int						=    flax_model.params["""params"""]
				else:
								UpperCamelCase_:				List[str]						=    flax_model.params
				UpperCamelCase_:				Tuple						=    flatten_dict(lowerCamelCase    )

				# add batch_stats keys,values to dict
				if "batch_stats" in flax_model.params:
								UpperCamelCase_:				Optional[Any]						=    flatten_dict(flax_model.params["""batch_stats"""]    )
								random_flax_state_dict.update(lowerCamelCase    )

				UpperCamelCase_:				Optional[int]						=    {}

				UpperCamelCase_:				Union[str, Any]						=    (model_prefix not in flax_model_params) and (
				    model_prefix in {k.split("""."""    )[0] for k in pt_state_dict.keys()}
				)
				UpperCamelCase_:				Dict						=    (model_prefix in flax_model_params) and (
				    model_prefix not in {k.split("""."""    )[0] for k in pt_state_dict.keys()}
				)

				# Need to change some parameters name to match Flax names
				for pt_key, pt_tensor in pt_state_dict.items():
								UpperCamelCase_:				List[Any]						=    tuple(pt_key.split("""."""    )    )

								# remove base model prefix if necessary
								UpperCamelCase_:				Optional[int]						=    pt_tuple_key[0] == model_prefix
								if load_model_with_head_into_base_model and has_base_model_prefix:
												UpperCamelCase_:				Tuple						=    pt_tuple_key[1:]

								# Correctly rename weight parameters
								UpperCamelCase_, UpperCamelCase_:				Union[str, Any]						=    rename_key_and_reshape_tensor(
								    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )

								# add model prefix if necessary
								UpperCamelCase_:				Any						=    (model_prefix,) + flax_key in random_flax_state_dict
								if load_base_model_into_model_with_head and require_base_model_prefix:
												UpperCamelCase_:				List[Any]						=    (model_prefix,) + flax_key

								if flax_key in random_flax_state_dict:
												if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
																raise ValueError(
																    F'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
																    F'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'''    )

        # add batch stats if the model contains batchnorm layers
								if "batch_stats" in flax_model.params:
												if "mean" in flax_key[-1] or "var" in flax_key[-1]:
																UpperCamelCase_:				List[str]						=    jnp.asarray(lowerCamelCase    )
																continue
												# remove num_batches_tracked key
												if "num_batches_tracked" in flax_key[-1]:
																flax_state_dict.pop(lowerCamelCase  ,     lowerCamelCase    )
																continue

												# also add unexpected weight so that warning is thrown
												UpperCamelCase_:				Dict						=    jnp.asarray(lowerCamelCase    )

								else:
												# also add unexpected weight so that warning is thrown
												UpperCamelCase_:				int						=    jnp.asarray(lowerCamelCase    )

				return unflatten_dict(lowerCamelCase    )







def 							A__       (    lowerCamelCase  ,     lowerCamelCase    )	->						Dict:
				import torch

				# Load the index
				UpperCamelCase_:				List[Any]						=    {}
				for shard_file in shard_filenames:
								# load using msgpack utils
								UpperCamelCase_:				List[str]						=    torch.load(lowerCamelCase    )
								UpperCamelCase_:				Optional[int]						=    {k: v.numpy() for k, v in pt_state_dict.items()}

								UpperCamelCase_:				List[str]						=    flax_model.base_model_prefix

								# use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
								if "batch_stats" in flax_model.params:
												UpperCamelCase_:				Union[str, Any]						=    flax_model.params["""params"""]

												UpperCamelCase_:				Any						=    flatten_dict(lowerCamelCase    )
												random_flax_state_dict.update(flatten_dict(flax_model.params["""batch_stats"""]    )    )
								else:
												UpperCamelCase_:				Union[str, Any]						=    flax_model.params
												UpperCamelCase_:				Optional[Any]						=    flatten_dict(lowerCamelCase    )

								UpperCamelCase_:				Tuple						=    (model_prefix not in flax_model_params) and (
								    model_prefix in {k.split("""."""    )[0] for k in pt_state_dict.keys()}
								)
								UpperCamelCase_:				Dict						=    (model_prefix in flax_model_params) and (
								    model_prefix not in {k.split("""."""    )[0] for k in pt_state_dict.keys()}
								)
								# Need to change some parameters name to match Flax names
								for pt_key, pt_tensor in pt_state_dict.items():
												UpperCamelCase_:				List[str]						=    tuple(pt_key.split("""."""    )    )

												# remove base model prefix if necessary
												UpperCamelCase_:				Dict						=    pt_tuple_key[0] == model_prefix
												if load_model_with_head_into_base_model and has_base_model_prefix:
																UpperCamelCase_:				Optional[int]						=    pt_tuple_key[1:]

												# Correctly rename weight parameters
												UpperCamelCase_, UpperCamelCase_:				int						=    rename_key_and_reshape_tensor(
												    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )
												# add model prefix if necessary
												UpperCamelCase_:				List[str]						=    (model_prefix,) + flax_key in random_flax_state_dict
												if load_base_model_into_model_with_head and require_base_model_prefix:
																UpperCamelCase_:				Optional[Any]						=    (model_prefix,) + flax_key

												if flax_key in random_flax_state_dict:
																if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
																				raise ValueError(
																				    F'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
																				    F'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'''    )

            # add batch stats if the model contains batchnorm layers
												if "batch_stats" in flax_model.params:
																if "mean" in flax_key[-1]:
																				UpperCamelCase_:				Dict						=    jnp.asarray(lowerCamelCase    )
																				continue
																if "var" in flax_key[-1]:
																				UpperCamelCase_:				Any						=    jnp.asarray(lowerCamelCase    )
																				continue
																# remove num_batches_tracked key
																if "num_batches_tracked" in flax_key[-1]:
																				flax_state_dict.pop(lowerCamelCase  ,     lowerCamelCase    )
																				continue

																# also add unexpected weight so that warning is thrown
																UpperCamelCase_:				Optional[int]						=    jnp.asarray(lowerCamelCase    )

												else:
																# also add unexpected weight so that warning is thrown
																UpperCamelCase_:				Optional[Any]						=    jnp.asarray(lowerCamelCase    )
				return unflatten_dict(lowerCamelCase    )







def 							A__       (    lowerCamelCase  ,     lowerCamelCase    )	->						Optional[Any]:
				UpperCamelCase_:				List[Any]						=    os.path.abspath(lowerCamelCase    )
				logger.info(F'''Loading Flax weights from {flax_checkpoint_path}'''    )

				# import correct flax class
				UpperCamelCase_:				Dict						=    getattr(lowerCamelCase  ,     """Flax""" + model.__class__.__name__    )

				# load flax weight dict
				with open(lowerCamelCase  ,     """rb"""    ) as state_f:
								try:
												UpperCamelCase_:				List[str]						=    from_bytes(lowerCamelCase  ,     state_f.read()    )
								except UnpicklingError:
												raise EnvironmentError(F'''Unable to convert {flax_checkpoint_path} to Flax deserializable object. '''    )

				return load_flax_weights_in_pytorch_model(lowerCamelCase  ,     lowerCamelCase    )







def 							A__       (    lowerCamelCase  ,     lowerCamelCase    )	->						str:

				try:
								import torch  # noqa: F401
				except ImportError:
								logger.error(
								    """Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see"""
								    """ https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"""
								    """ instructions."""    )
								raise

				# check if we have bf16 weights
				UpperCamelCase_:				Tuple						=    flatten_dict(jax.tree_util.tree_map(lambda lowerCamelCase    : x.dtype == jnp.bfloataa  ,     lowerCamelCase    )    ).values()
				if any(lowerCamelCase    ):
								# convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
								# and bf16 is not fully supported in PT yet.
								logger.warning(
								    """Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` """
								    """before loading those in PyTorch model."""    )
								UpperCamelCase_:				Tuple						=    jax.tree_util.tree_map(
								    lambda lowerCamelCase    : params.astype(np.floataa    ) if params.dtype == jnp.bfloataa else params  ,     lowerCamelCase    )

				UpperCamelCase_:				Dict						=    flatten_dict(lowerCamelCase    )
				UpperCamelCase_:				List[Any]						=    pt_model.state_dict()

				UpperCamelCase_:				Dict						=    (pt_model.base_model_prefix in flax_state) and (
				    pt_model.base_model_prefix not in {k.split("""."""    )[0] for k in pt_model_dict.keys()}
				)
				UpperCamelCase_:				Optional[int]						=    (pt_model.base_model_prefix not in flax_state) and (
				    pt_model.base_model_prefix in {k.split("""."""    )[0] for k in pt_model_dict.keys()}
				)

				# keep track of unexpected & missing keys
				UpperCamelCase_:				Tuple						=    []
				UpperCamelCase_:				Union[str, Any]						=    set(pt_model_dict.keys()    )

				for flax_key_tuple, flax_tensor in flax_state_dict.items():
								UpperCamelCase_:				Any						=    flax_key_tuple[0] == pt_model.base_model_prefix
								UpperCamelCase_:				List[Any]						=    """.""".join((pt_model.base_model_prefix,) + flax_key_tuple    ) in pt_model_dict

								# adapt flax_key to prepare for loading from/to base model only
								if load_model_with_head_into_base_model and has_base_model_prefix:
												UpperCamelCase_:				List[Any]						=    flax_key_tuple[1:]
								elif load_base_model_into_model_with_head and require_base_model_prefix:
												UpperCamelCase_:				List[str]						=    (pt_model.base_model_prefix,) + flax_key_tuple

								# rename flax weights to PyTorch format
								if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(lowerCamelCase    ) not in pt_model_dict:
												# conv layer
												UpperCamelCase_:				Tuple						=    flax_key_tuple[:-1] + ("""weight""",)
												UpperCamelCase_:				str						=    jnp.transpose(lowerCamelCase  ,     (3, 2, 0, 1)    )
								elif flax_key_tuple[-1] == "kernel" and ".".join(lowerCamelCase    ) not in pt_model_dict:
												# linear layer
												UpperCamelCase_:				Tuple						=    flax_key_tuple[:-1] + ("""weight""",)
												UpperCamelCase_:				Union[str, Any]						=    flax_tensor.T
								elif flax_key_tuple[-1] in ["scale", "embedding"]:
												UpperCamelCase_:				List[Any]						=    flax_key_tuple[:-1] + ("""weight""",)

								# adding batch stats from flax batch norm to pt
								elif "mean" in flax_key_tuple[-1]:
												UpperCamelCase_:				List[str]						=    flax_key_tuple[:-1] + ("""running_mean""",)
								elif "var" in flax_key_tuple[-1]:
												UpperCamelCase_:				Dict						=    flax_key_tuple[:-1] + ("""running_var""",)

								if "batch_stats" in flax_state:
												UpperCamelCase_:				int						=    """.""".join(flax_key_tuple[1:]    )  # Remove the params/batch_stats header
								else:
												UpperCamelCase_:				int						=    """.""".join(lowerCamelCase    )

								# We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
								UpperCamelCase_:				Union[str, Any]						=    {}
								# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
								for key in pt_model_dict:
												UpperCamelCase_:				int						=    key.split("""."""    )
												UpperCamelCase_:				Tuple						=    None
												if key_components[-3::2] == ["parametrizations", "original0"]:
																UpperCamelCase_:				List[str]						=    key_components[-2] + """_g"""
												elif key_components[-3::2] == ["parametrizations", "original1"]:
																UpperCamelCase_:				Tuple						=    key_components[-2] + """_v"""
												if name is not None:
																UpperCamelCase_:				Tuple						=    key_components[:-3] + [name]
																UpperCamelCase_:				Union[str, Any]						=    """.""".join(lowerCamelCase    )
																UpperCamelCase_:				Any						=    key

								if flax_key in special_pt_names:
												UpperCamelCase_:				Union[str, Any]						=    special_pt_names[flax_key]

								if flax_key in pt_model_dict:
												if flax_tensor.shape != pt_model_dict[flax_key].shape:
																raise ValueError(
																    F'''Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '''
																    F'''to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.'''    )
												else:
																# add weight to pytorch dict
																UpperCamelCase_:				List[Any]						=    np.asarray(lowerCamelCase    ) if not isinstance(lowerCamelCase  ,     np.ndarray    ) else flax_tensor
																UpperCamelCase_:				Dict						=    torch.from_numpy(lowerCamelCase    )
																# remove from missing keys
																missing_keys.remove(lowerCamelCase    )
								else:
												# weight is not expected by PyTorch model
												unexpected_keys.append(lowerCamelCase    )

				pt_model.load_state_dict(lowerCamelCase    )

				# re-transform missing_keys to list
				UpperCamelCase_:				int						=    list(lowerCamelCase    )

				if len(lowerCamelCase    ) > 0:
								logger.warning(
								    """Some weights of the Flax model were not used when initializing the PyTorch model"""
								    F''' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'''
								    F''' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'''
								    """ (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"""
								    F''' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'''
								    """ to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"""
								    """ FlaxBertForSequenceClassification model)."""    )
				else:
								logger.warning(F'''All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n'''    )
				if len(lowerCamelCase    ) > 0:
								logger.warning(
								    F'''Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'''
								    F''' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'''
								    """ use it for predictions and inference."""    )
				else:
								logger.warning(
								    F'''All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n'''
								    """If your task is similar to the task the model of the checkpoint was trained on, """
								    F'''you can already use {pt_model.__class__.__name__} for predictions without further training.'''    )

				return pt_model








import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin

@skip_mps
class 							_UpperCamelCase					(       _A  ,							_A  ,							unittest.TestCase	):






			'''simple docstring'''

			__UpperCamelCase   :    str          =			IFInpaintingSuperResolutionPipeline
			__UpperCamelCase   :    str          =			TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
			__UpperCamelCase   :    Optional[Any]          =			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"""original_image"""}	)
			__UpperCamelCase   :    Dict          =			PipelineTesterMixin.required_optional_params - {"""latents"""}

			def    lowerCAmelCase__						(							self			:							Union[str, Any]   ):
							return self._get_superresolution_dummy_components()

			def    lowerCAmelCase__						(							self			:							Dict    ,						snake_case_			:							Optional[int]    ,						snake_case_			:							str=0   ):
							if str(snake_case_   ).startswith("""mps"""   ):
											UpperCamelCase_:				Union[str, Any]						=    torch.manual_seed(snake_case_   )
							else:
											UpperCamelCase_:				Tuple						=    torch.Generator(device=snake_case_   ).manual_seed(snake_case_   )

							UpperCamelCase_:				Dict						=    floats_tensor((1, 3, 16, 16)    ,						rng=random.Random(snake_case_   )   ).to(snake_case_   )
							UpperCamelCase_:				int						=    floats_tensor((1, 3, 32, 32)    ,						rng=random.Random(snake_case_   )   ).to(snake_case_   )
							UpperCamelCase_:				str						=    floats_tensor((1, 3, 32, 32)    ,						rng=random.Random(snake_case_   )   ).to(snake_case_   )

							UpperCamelCase_:				Dict						=    {
							    """prompt""": """A painting of a squirrel eating a burger""",
							    """image""": image,
							    """original_image""": original_image,
							    """mask_image""": mask_image,
							    """generator""": generator,
							    """num_inference_steps""": 2,
							    """output_type""": """numpy""",
							}

							return inputs

			@unittest.skipIf(
			    torch_device != """cuda""" or not is_xformers_available()    ,						reason="""XFormers attention is only available with CUDA and `xformers` installed"""    ,						)
			def    lowerCAmelCase__						(							self			:							Tuple   ):
							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3   )

			def    lowerCAmelCase__						(							self			:							Union[str, Any]   ):
							self._test_save_load_optional_components()

			@unittest.skipIf(torch_device != """cuda"""    ,						reason="""float16 requires CUDA"""   )
			def    lowerCAmelCase__						(							self			:							List[Any]   ):
							# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
							super().test_save_load_floataa(expected_max_diff=1e-1   )

			def    lowerCAmelCase__						(							self			:							str   ):
							self._test_attention_slicing_forward_pass(expected_max_diff=1e-2   )

			def    lowerCAmelCase__						(							self			:							str   ):
							self._test_save_load_local()




			def    lowerCAmelCase__						(							self			:							Union[str, Any]   ):
							self._test_inference_batch_single_identical(
							    expected_max_diff=1e-2    ,						)






